Commonly Asked Data Structure Interview Questions on Heap Data Structure
Last Updated : 27 Feb, 2025
A heap is a complete binary tree that maintains a specific order, making it efficient for priority-based operations. It is mainly of two types:
- Min Heap: The smallest value is at the root, and each parent is smaller than its children.
- Max Heap: The largest value is at the root, and each parent is larger than its children.
Heaps are commonly stored as arrays, where the parent-child relationship follows a simple index formula. Heaps are commonly used in priority queues and sorting (Heap Sort). They allow efficient insertion, deletion, and retrieval of the smallest or largest element.
Theoretical Questions for Interviews on Heap
1. What is a heap data structure?
A heap is a complete binary tree that satisfies the heap property: each node’s value is greater than or equal to its children’s values.
2. What are the two types of heaps?
In a max-heap, the root node has the maximum value, while in a min-heap, the root node has the minimum value.
3. What is the time complexity of inserting an element into a heap?
O(log n), where n is the number of elements in the heap.
Refer Insertion and Deletion in Heaps for more
4. What is the time complexity of deleting an element from a heap?
O(log n), where n is the number of elements in the heap.
Refer Insertion and Deletion in Heaps for more
5. What is the time complexity of finding the minimum or maximum element in a heap?
The time complexity of finding the minimum or maximum element in a heap depends on whether the heap is a min-heap or max-heap and its structure:
- Min-Heap:
- The minimum element is always at the root (index
0 in an array representation). - Time Complexity: O(1) (constant time).
- Max-Heap:
- The maximum element is always at the root.
- Time Complexity: O(1) (constant time).
6. What is the time complexity of finding the maximum element in a Min-Heap or the minimum element in a Max-Heap?
In a Min-Heap, the maximum element is located among the leaf nodes, requiring a scan of approximately n/2 elements. Therefore, the time complexity is O(n). Similarly, in a Max-Heap, finding the minimum element also takes O(n) time, as it is among the leaf nodes.
Refer Maximum element in min heap for more
7. What are the applications of heaps?
Heap applications:
8. What is the difference between a heap and a binary search tree (BST)?
A heap is a complete binary tree that satisfies the heap property, while a BST is a partially ordered binary tree that satisfies the BST property.
9. How do you convert a BST into a heap?
By performing an in-order traversal of the BST and inserting the elements into a heap.
Refer Convert BST to Min Heap for more
10. How do you merge two heaps?
By creating a new heap and inserting the elements from both heaps into the new heap while maintaining the heap property.
11. What is the difference between a heap and a priority queue?
A heap is a data structure, while a priority queue is an abstract data type that can be implemented using a heap.
12. What are the advantages of using a heap?
Advantages of using a heap:
- Efficient insertion and extraction (O(log n))
- Can be used to implement priority queues
- Can be used for sorting (O(n log n))
- Useful for other applications, such as finding the median and implementing Dijkstra’s algorithm
13. What is Heap Sort, and how does it work?
Heap Sort is a comparison-based sorting algorithm that uses a binary heap to repeatedly extract the largest (or smallest) element and place it at the end of the array.
14. What are the key steps involved in Heap Sort?
- Build a max heap from the input data.
- Extract the maximum element (root) and place it at the end.
- Heapify the remaining heap and repeat until sorted.
15. What is the time complexity of Heap Sort in the worst, average, and best cases?
Best, Average, and Worst Case: O(n log n) since building the heap takes O(n) and each extraction takes O(log n).
16. Why is Heap Sort preferred for external sorting?
Because of its O(n log n) time complexity and in-place property, making it efficient for handling large datasets.
Top Coding Interview Questions on Heap
The following list of 50 heap coding problems covers a range of difficulty levels, from easy to hard, to help candidates prepare for interviews.
Top 50 Problems on Heap Data Structure asked in SDE Interviews
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